Triple

T36971752
Position Surface form Disambiguated ID Type / Status
Subject Dorothea Gerard E914586 entity
Predicate memberOf P10 FINISHED
Object Gerard literary family
The Gerard literary family was a notable 19th-century writing dynasty, best known for its women novelists who contributed popular fiction and historical romances to British and European literature.
 E2206575 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Gerard literary family | Statement: [Dorothea Gerard, memberOf, Gerard literary family]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Gerard literary family
Triple: [Dorothea Gerard, memberOf, Gerard literary family]
Generated description
The Gerard literary family was a notable 19th-century writing dynasty, best known for its women novelists who contributed popular fiction and historical romances to British and European literature.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f76e8d13b4819089af24a47ce092fc completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9ff487e708190b52f785b242700ee completed May 5, 2026, 2:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e2c47f7cc81908fc9bd4a3a8c1791 completed June 26, 2026, 7:37 a.m.
NEDg Description generation batch_6a3e2cf5b99c8190b0f2573e57f9d5d7 completed June 26, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_6a3e43348bd88190bd7b9886e07e2a65 completed June 26, 2026, 9:15 a.m.
Created at: May 3, 2026, 4:14 p.m.